Data Science & Analytics

Overview

We apply data science and analytics to strengthen mechanistic models and support data-driven decision-making in public health and healthcare systems. Our work emphasizes interpretability, uncertainty quantification, and real-world applicability.

Research Focus

  • Statistical modeling and Bayesian inference
  • Machine learning for prediction and risk stratification
  • Spatiotemporal analysis and disease surveillance
  • Model validation and uncertainty quantification

Selected Publications

  • Comparative analysis of machine learning models for predicting hospital- and community-associated urinary tract infections
    Journal of Hospital Infection (2025)
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  • Bayesian inference of nosocomial MRSA transmission rates in an urban safety-net hospital
    Journal of Hospital Infection (2025)
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  • Patient flow modeling and simulation to study HAI incidence in an Emergency Department
    Smart Health (2024)
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Program Support